CE3: Customizable and Easily Extensible Ensemble Tool for Motif Discovery

Ensemble methods (or simply ensembles) for motif discov- ery represent a relatively new approach to improve the ac- curacy of stand-alone motif finders. The performance of an ensemble is clearly determined by the included finders as well as the strategy to combine the results returned by the latter (the so called learning rule). A potential obstacle to a widespread adoption of ensembles is that the choice of the particular finders included is closed. Although possible in principle, the addition to an ensemble of a new "promising" tool requires knowledge of the internals of the ensemble and usually non trivial programming skills. In this research we propose a general architecture for ensem- bles and a prototype called CE3: Customizable and Easily Extensible Ensemble, which is meant to be extensible and customizable at the level of the two key components mod- ules namely external tools finding and learning rule. In this way the user will be able to essentially "simulate" any ex- isting ensemble, create his/her own ensemble according to his/her preferences on finding tools and learning functions and, finally, keep it up to date when new tools and new ideas for learning functions are proposed in literature. These fea- tures also make CE3 a suitable tool to perform experiments that may lead to a proper configuration of ensembles in the research of novel motifs